library(readr)
library(dplyr)
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## Attaching package: 'dplyr'
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## filter, lag
## The following objects are masked from 'package:base':
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library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ stringr 1.4.0
## ✓ tidyr 1.1.4 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggplot2)
library(janitor)
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## chisq.test, fisher.test
library(plotly)
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library(gganimate)
library(ggthemes)
Reading in:
whr <- read_csv("world-happiness-report.csv")
## Rows: 1949 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Country name
## dbl (10): year, Life Ladder, Log GDP per capita, Social support, Healthy lif...
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TidyTuesday:
countries_plot <- whr %>%
clean_names() %>%
filter(country_name %in% c("Colombia","Philippines","China")) %>%
group_by(country_name) %>%
summarise(average_GDP = sum(log_gdp_per_capita)/n()) %>%
ggplot()+
geom_col(aes(x=country_name,y=average_GDP, fill= country_name))+
labs(title="Pia's, Adeline's and Marcela's Countries GDP's per Capita",
x= " ",
y= " ")+
theme_minimal()+
theme(legend.position = "none")
ggplotly(countries_plot)